Instrumentation & Measurement Magazine 24-4 - 32

Fig. 6. Error diffusion on both techniques.
and verify its reduction. Reducing quantization levels means
the decreasing of chromatism rate, and then the image definition,
as depicted in Fig. 6. The performance of the presented
processing techniques can be computed over the whole image
or over a small segment of the image to provide a localized
measure of performance.
We can define then:
E zw
N
1
A 
ij 
( ,)ij A
 
,,
ij
2
(3)
where A is the subset of the image containing N pixels and
zi,j
higher in monadic than in PIV;
and wi,j are the diffusion outputs and used windows, respectively.
Different EA
this means monadic imaging includes features that can be
printed in any hue, that also means black, grey levels and
color. On the other hand, low level of EA
indicates the image
can be printed only in color. It also means the PIV image
contains features to be distinguished only in color. Certainly,
PIV better shows the functionalization process of
nanoparticles.
Technique
Table 1 - Performance of error diffusion
Image 1
Monadic processing
PIV
32
0.9766
0.707
Image 2
0.9933
0.4981
values are given in the table below
for both PIV and monadic processing on three image samples.
Table 1 shows an EA
Now it is time to make a comparison between both processing
techniques of processing given the dimensions and the
shape illustrated before. Both algorithms detect shape deformation
and related expanding swarm due to functionalization
imperfections. But PIV is able, as we can see, to detect more
nano-variations in material homogeneity than the monadic
approach. That is, changes in eventual consistency and possible
subsidence. Images 2 and 3 of Fig. 4 and Fig. 5 show this
aspect. In Fig. 4, it is depicted as surface ring or shroud, but in
Fig. 5, we can see what is inside with major details. As with
performance, in terms of dimension, using the uncertainty approach,
we can make a comparison between both techniques.
The propagation of uncertainty of different images (Xi
) to the
uncertainty of Y was performed in this paper using linear
approximation, as outlined in the Guide to Expression of Uncertainty
in Measurement (GUM) [14].
Equation (4) is basically the simple substitution based on
GUM's framework:
Uy
[]
 

 Xi

U[]
()g Xi
i 
x
Image 3
0.987
0.5253
The result shows that significant deviation can be observed
while using PIV algorithm on high grey contrasted images.
However, monadic processing is less sensitive to low changes in
grey scale (Fig. 7). In fact, going back to Fig. 6, all images of PIV
techniques exhibit lower diffusion than monadic ones. In particular,
PIV image 1 is blurred more than the counterpart one in
monadic; this statement clearly justifies the difference in terms
of uncertainty between the images marked 1 in both figures.
IEEE Instrumentation & Measurement Magazine
June 2021
2
(4)

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